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      Psychometric properties of the five facet mindfulness questionnaire in depressed adults and development of a short form.

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          Abstract

          In recent years, there has been a growing interest in therapies that include the learning of mindfulness skills. The 39-item Five Facet Mindfulness Questionnaire (FFMQ) has been developed as a reliable and valid comprehensive instrument for assessing different aspects of mindfulness in community and student samples. In this study, the psychometric properties of the Dutch FFMQ were assessed in a sample of 376 adults with clinically relevant symptoms of depression and anxiety. Construct validity was examined with confirmatory factor analyses and by relating the FFMQ to measures of psychological symptoms, well-being, experiential avoidance, and the personality factors neuroticism and openness to experience. In addition, a 24-item short form of the FFMQ (FFMQ-SF) was developed and assessed in the same sample and cross-validated in an independent sample of patients with fibromyalgia. Confirmatory factor analyses showed acceptable model fit for a correlated five-factor structure of the FFMQ and good model fit for the structure of the FFMQ-SF. The replicability of the five-factor structure of the FFMQ-SF was confirmed in the fibromyalgia sample. Both instruments proved highly sensitive to change. It is concluded that both the FFMQ and the FFMQ-SF are reliable and valid instruments for use in adults with clinically relevant symptoms of depression and anxiety.

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          Author and article information

          Journal
          Assessment
          Assessment
          SAGE Publications
          1552-3489
          1073-1911
          Sep 2011
          : 18
          : 3
          Affiliations
          [1 ] Psychology and Communication of Health and Risk, Faculty of Behavioural Sciences (Citadel), University of Twente, Enschede, Netherlands. e.t.bohlmeijer@utwente.nl
          Article
          1073191111408231
          10.1177/1073191111408231
          21586480
          82f32ed3-8d1a-4d90-9e79-e63817d9fa0a
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